{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "year=2019\n",
    "month=11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sys\n",
    "sys.path.append('../../py')\n",
    "import db\n",
    "import weighted\n",
    "import inspect\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_original=pd.read_sql(sql=f\"select * from _{year}{month:02} where monthly_salary>0 and monthly_salary<80000\", con=db.get_conn())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(84686, 94)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_original.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data_original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "del data['publish_date']\n",
    "del data['published_on_weekend']\n",
    "del data['title']\n",
    "del data['company_title']\n",
    "del data['company_description']\n",
    "del data['job_description']\n",
    "del data['job_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sub_stats_by_prefix(data, prefix):\n",
    "    \n",
    "    features = [feature for feature in data.columns if feature.startswith(prefix)]\n",
    "    salary_mean=[]\n",
    "    salary_median=[]\n",
    "    salary_95_min=[]\n",
    "    salary_95_max=[]\n",
    "    count=[]\n",
    "    \n",
    "    features_out=[]\n",
    "    for feature in features:\n",
    "        #print(feature)\n",
    "        idata=data[data[feature]==1]\n",
    "        headcount=idata.headcount.sum()\n",
    "        values = idata.monthly_salary.values\n",
    "        weights = idata.headcount.values\n",
    "        #print(str(headcount))\n",
    "        if headcount==0:\n",
    "            continue\n",
    "        \n",
    "        salary_mean.append(weighted.weighted_mean(values, weights))\n",
    "        q = weighted.weighted_quantile(values,[0.025,0.5,0.975],weights)\n",
    "        salary_median.append(q[1])\n",
    "        salary_95_min.append(q[0])\n",
    "        salary_95_max.append(q[2])\n",
    "        count.append(idata.headcount.sum())\n",
    "        features_out.append(feature)\n",
    "    sub_data=pd.DataFrame()\n",
    "    sub_data['rank']=range(0,len(features_out))\n",
    "    sub_data[prefix]=[f.replace(prefix,'') for f in features_out]\n",
    "    sub_data['salary_mean']=salary_mean\n",
    "    sub_data['salary_median']=salary_median\n",
    "    sub_data['salary_95_min']=salary_95_min\n",
    "    sub_data['salary_95_max']=salary_95_max\n",
    "    sub_data['head_count']=count\n",
    "    sub_data['percentage']=count/np.sum(count)\n",
    "    sub_data=sub_data.sort_values(by='salary_mean', ascending=False)\n",
    "    sub_data['rank']=range(1,len(features_out)+1)\n",
    "    #sub_data=sub_data.reset_index()\n",
    "    return sub_data\n",
    "\n",
    "def apply_style(sub_data):\n",
    "    return sub_data.style.hide_index().format(\n",
    "        {\"percentage\":\"{:.2%}\",\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\",\"salary_95_min\":\"{:.0f}\",\"salary_95_max\":\"{:.0f}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_pl=get_sub_stats_by_prefix(data,'pl_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >rank</th>        <th class=\"col_heading level0 col1\" >pl_</th>        <th class=\"col_heading level0 col2\" >salary_mean</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_min</th>        <th class=\"col_heading level0 col5\" >salary_95_max</th>        <th class=\"col_heading level0 col6\" >head_count</th>        <th class=\"col_heading level0 col7\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow0_col0\" class=\"data row0 col0\" >1</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow0_col1\" class=\"data row0 col1\" >rust</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow0_col2\" class=\"data row0 col2\" >20090</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow0_col3\" class=\"data row0 col3\" >17500</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow0_col4\" class=\"data row0 col4\" >5318</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow0_col5\" class=\"data row0 col5\" >45000</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow0_col6\" class=\"data row0 col6\" >394</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow0_col7\" class=\"data row0 col7\" >0.10%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow1_col0\" class=\"data row1 col0\" >2</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow1_col1\" class=\"data row1 col1\" >lua</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow1_col2\" class=\"data row1 col2\" >17679</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow1_col3\" class=\"data row1 col3\" >16000</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow1_col4\" class=\"data row1 col4\" >5250</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow1_col5\" class=\"data row1 col5\" >35000</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow1_col6\" class=\"data row1 col6\" >3146</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow1_col7\" class=\"data row1 col7\" >0.83%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow2_col0\" class=\"data row2 col0\" >3</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow2_col1\" class=\"data row2 col1\" >go</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow2_col2\" class=\"data row2 col2\" >17627</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow2_col3\" class=\"data row2 col3\" >15000</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow2_col4\" class=\"data row2 col4\" >5500</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow2_col5\" class=\"data row2 col5\" >40000</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow2_col6\" class=\"data row2 col6\" >27369</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow2_col7\" class=\"data row2 col7\" >7.25%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow3_col0\" class=\"data row3 col0\" >4</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow3_col1\" class=\"data row3 col1\" >matlab</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow3_col2\" class=\"data row3 col2\" >17545</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow3_col3\" class=\"data row3 col3\" >16666</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow3_col4\" class=\"data row3 col4\" >5250</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow3_col5\" class=\"data row3 col5\" >38932</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow3_col6\" class=\"data row3 col6\" >5505</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow3_col7\" class=\"data row3 col7\" >1.46%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow4_col0\" class=\"data row4 col0\" >5</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow4_col1\" class=\"data row4 col1\" >python</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow4_col2\" class=\"data row4 col2\" >17490</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow4_col3\" class=\"data row4 col3\" >15000</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow4_col4\" class=\"data row4 col4\" >3750</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow4_col5\" class=\"data row4 col5\" >41666</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow4_col6\" class=\"data row4 col6\" >29916</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow4_col7\" class=\"data row4 col7\" >7.93%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow5_col0\" class=\"data row5 col0\" >6</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow5_col1\" class=\"data row5 col1\" >ruby</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow5_col2\" class=\"data row5 col2\" >16279</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow5_col3\" class=\"data row5 col3\" >15000</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow5_col4\" class=\"data row5 col4\" >2500</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow5_col5\" class=\"data row5 col5\" >35000</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow5_col6\" class=\"data row5 col6\" >1098</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow5_col7\" class=\"data row5 col7\" >0.29%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow6_col0\" class=\"data row6 col0\" >7</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow6_col1\" class=\"data row6 col1\" >kotlin</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow6_col2\" class=\"data row6 col2\" >16082</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow6_col3\" class=\"data row6 col3\" >15000</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow6_col4\" class=\"data row6 col4\" >5545</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow6_col5\" class=\"data row6 col5\" >35000</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow6_col6\" class=\"data row6 col6\" >1225</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow6_col7\" class=\"data row6 col7\" >0.32%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow7_col0\" class=\"data row7 col0\" >8</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow7_col1\" class=\"data row7 col1\" >haskell</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow7_col2\" class=\"data row7 col2\" >16040</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow7_col3\" class=\"data row7 col3\" >15000</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow7_col4\" class=\"data row7 col4\" >7500</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow7_col5\" class=\"data row7 col5\" >25000</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow7_col6\" class=\"data row7 col6\" >50</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow7_col7\" class=\"data row7 col7\" >0.01%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow8_col0\" class=\"data row8 col0\" >9</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow8_col1\" class=\"data row8 col1\" >perl</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow8_col2\" class=\"data row8 col2\" >15949</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow8_col3\" class=\"data row8 col3\" >13500</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow8_col4\" class=\"data row8 col4\" >4500</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow8_col5\" class=\"data row8 col5\" >37500</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow8_col6\" class=\"data row8 col6\" >2405</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow8_col7\" class=\"data row8 col7\" >0.64%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow9_col0\" class=\"data row9 col0\" >10</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow9_col1\" class=\"data row9 col1\" >cpp</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow9_col2\" class=\"data row9 col2\" >15461</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow9_col3\" class=\"data row9 col3\" >12916</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow9_col4\" class=\"data row9 col4\" >3750</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow9_col5\" class=\"data row9 col5\" >37500</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow9_col6\" class=\"data row9 col6\" >61960</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow9_col7\" class=\"data row9 col7\" >16.42%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow10_col0\" class=\"data row10 col0\" >11</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow10_col1\" class=\"data row10 col1\" >swift</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow10_col2\" class=\"data row10 col2\" >15073</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow10_col3\" class=\"data row10 col3\" >12500</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow10_col4\" class=\"data row10 col4\" >6000</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow10_col5\" class=\"data row10 col5\" >30438</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow10_col6\" class=\"data row10 col6\" >3239</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow10_col7\" class=\"data row10 col7\" >0.86%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow11_col0\" class=\"data row11 col0\" >12</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow11_col1\" class=\"data row11 col1\" >julia</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow11_col2\" class=\"data row11 col2\" >15071</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow11_col3\" class=\"data row11 col3\" >15071</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow11_col4\" class=\"data row11 col4\" >12500</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow11_col5\" class=\"data row11 col5\" >21500</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow11_col6\" class=\"data row11 col6\" >7</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow11_col7\" class=\"data row11 col7\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow12_col0\" class=\"data row12 col0\" >13</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow12_col1\" class=\"data row12 col1\" >objective_c</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow12_col2\" class=\"data row12 col2\" >14001</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow12_col3\" class=\"data row12 col3\" >12500</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow12_col4\" class=\"data row12 col4\" >5175</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow12_col5\" class=\"data row12 col5\" >24250</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow12_col6\" class=\"data row12 col6\" >296</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow12_col7\" class=\"data row12 col7\" >0.08%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow13_col0\" class=\"data row13 col0\" >14</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow13_col1\" class=\"data row13 col1\" >typescript</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow13_col2\" class=\"data row13 col2\" >13996</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow13_col3\" class=\"data row13 col3\" >12500</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow13_col4\" class=\"data row13 col4\" >6000</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow13_col5\" class=\"data row13 col5\" >30000</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow13_col6\" class=\"data row13 col6\" >904</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow13_col7\" class=\"data row13 col7\" >0.24%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow14_col0\" class=\"data row14 col0\" >15</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow14_col1\" class=\"data row14 col1\" >php</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow14_col2\" class=\"data row14 col2\" >13473</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow14_col3\" class=\"data row14 col3\" >11757</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow14_col4\" class=\"data row14 col4\" >3750</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow14_col5\" class=\"data row14 col5\" >35000</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow14_col6\" class=\"data row14 col6\" >16494</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow14_col7\" class=\"data row14 col7\" >4.37%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow15_col0\" class=\"data row15 col0\" >16</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow15_col1\" class=\"data row15 col1\" >java</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow15_col2\" class=\"data row15 col2\" >13340</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow15_col3\" class=\"data row15 col3\" >12500</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow15_col4\" class=\"data row15 col4\" >3750</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow15_col5\" class=\"data row15 col5\" >30000</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow15_col6\" class=\"data row15 col6\" >126905</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow15_col7\" class=\"data row15 col7\" >33.62%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow16_col0\" class=\"data row16 col0\" >17</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow16_col1\" class=\"data row16 col1\" >javascript</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow16_col2\" class=\"data row16 col2\" >11644</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow16_col3\" class=\"data row16 col3\" >11000</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow16_col4\" class=\"data row16 col4\" >3750</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow16_col5\" class=\"data row16 col5\" >25000</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow16_col6\" class=\"data row16 col6\" >46447</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow16_col7\" class=\"data row16 col7\" >12.31%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow17_col0\" class=\"data row17 col0\" >18</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow17_col1\" class=\"data row17 col1\" >c_sharp</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow17_col2\" class=\"data row17 col2\" >11476</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow17_col3\" class=\"data row17 col3\" >10500</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow17_col4\" class=\"data row17 col4\" >3750</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow17_col5\" class=\"data row17 col5\" >26500</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow17_col6\" class=\"data row17 col6\" >48384</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow17_col7\" class=\"data row17 col7\" >12.82%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow18_col0\" class=\"data row18 col0\" >19</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow18_col1\" class=\"data row18 col1\" >visual_basic</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow18_col2\" class=\"data row18 col2\" >10968</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow18_col3\" class=\"data row18 col3\" >10562</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow18_col4\" class=\"data row18 col4\" >4500</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow18_col5\" class=\"data row18 col5\" >21787</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow18_col6\" class=\"data row18 col6\" >39</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow18_col7\" class=\"data row18 col7\" >0.01%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow19_col0\" class=\"data row19 col0\" >20</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow19_col1\" class=\"data row19 col1\" >vba</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow19_col2\" class=\"data row19 col2\" >10678</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow19_col3\" class=\"data row19 col3\" >10000</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow19_col4\" class=\"data row19 col4\" >2500</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow19_col5\" class=\"data row19 col5\" >23300</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow19_col6\" class=\"data row19 col6\" >408</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow19_col7\" class=\"data row19 col7\" >0.11%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow20_col0\" class=\"data row20 col0\" >21</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow20_col1\" class=\"data row20 col1\" >delphi</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow20_col2\" class=\"data row20 col2\" >10588</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow20_col3\" class=\"data row20 col3\" >9000</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow20_col4\" class=\"data row20 col4\" >3750</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow20_col5\" class=\"data row20 col5\" >21146</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow20_col6\" class=\"data row20 col6\" >1265</td>\n",
       "                        <td id=\"T_ad1617ec_fd74_11e9_b53f_701ce71031efrow20_col7\" class=\"data row20 col7\" >0.34%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x293e8122888>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apply_style(data_pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21, 8)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_pl=data_pl[['pl_','percentage']].sort_values(by='percentage', ascending=False)\n",
    "data_pl.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >rank</th>        <th class=\"col_heading level0 col1\" >pl_</th>        <th class=\"col_heading level0 col2\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow0_col0\" class=\"data row0 col0\" >1</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow0_col1\" class=\"data row0 col1\" >java</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow0_col2\" class=\"data row0 col2\" >33.62%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow1_col0\" class=\"data row1 col0\" >2</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow1_col1\" class=\"data row1 col1\" >cpp</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow1_col2\" class=\"data row1 col2\" >16.42%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow2_col0\" class=\"data row2 col0\" >3</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow2_col1\" class=\"data row2 col1\" >c_sharp</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow2_col2\" class=\"data row2 col2\" >12.82%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow3_col0\" class=\"data row3 col0\" >4</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow3_col1\" class=\"data row3 col1\" >javascript</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow3_col2\" class=\"data row3 col2\" >12.31%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow4_col0\" class=\"data row4 col0\" >5</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow4_col1\" class=\"data row4 col1\" >python</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow4_col2\" class=\"data row4 col2\" >7.93%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow5_col0\" class=\"data row5 col0\" >6</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow5_col1\" class=\"data row5 col1\" >go</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow5_col2\" class=\"data row5 col2\" >7.25%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow6_col0\" class=\"data row6 col0\" >7</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow6_col1\" class=\"data row6 col1\" >php</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow6_col2\" class=\"data row6 col2\" >4.37%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow7_col0\" class=\"data row7 col0\" >8</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow7_col1\" class=\"data row7 col1\" >matlab</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow7_col2\" class=\"data row7 col2\" >1.46%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow8_col0\" class=\"data row8 col0\" >9</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow8_col1\" class=\"data row8 col1\" >swift</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow8_col2\" class=\"data row8 col2\" >0.86%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow9_col0\" class=\"data row9 col0\" >10</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow9_col1\" class=\"data row9 col1\" >lua</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow9_col2\" class=\"data row9 col2\" >0.83%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow10_col0\" class=\"data row10 col0\" >11</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow10_col1\" class=\"data row10 col1\" >perl</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow10_col2\" class=\"data row10 col2\" >0.64%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow11_col0\" class=\"data row11 col0\" >12</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow11_col1\" class=\"data row11 col1\" >delphi</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow11_col2\" class=\"data row11 col2\" >0.34%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow12_col0\" class=\"data row12 col0\" >13</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow12_col1\" class=\"data row12 col1\" >kotlin</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow12_col2\" class=\"data row12 col2\" >0.32%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow13_col0\" class=\"data row13 col0\" >14</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow13_col1\" class=\"data row13 col1\" >ruby</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow13_col2\" class=\"data row13 col2\" >0.29%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow14_col0\" class=\"data row14 col0\" >15</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow14_col1\" class=\"data row14 col1\" >typescript</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow14_col2\" class=\"data row14 col2\" >0.24%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow15_col0\" class=\"data row15 col0\" >16</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow15_col1\" class=\"data row15 col1\" >vba</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow15_col2\" class=\"data row15 col2\" >0.11%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow16_col0\" class=\"data row16 col0\" >17</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow16_col1\" class=\"data row16 col1\" >rust</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow16_col2\" class=\"data row16 col2\" >0.10%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow17_col0\" class=\"data row17 col0\" >18</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow17_col1\" class=\"data row17 col1\" >objective_c</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow17_col2\" class=\"data row17 col2\" >0.08%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow18_col0\" class=\"data row18 col0\" >19</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow18_col1\" class=\"data row18 col1\" >haskell</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow18_col2\" class=\"data row18 col2\" >0.01%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow19_col0\" class=\"data row19 col0\" >20</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow19_col1\" class=\"data row19 col1\" >visual_basic</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow19_col2\" class=\"data row19 col2\" >0.01%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow20_col0\" class=\"data row20 col0\" >21</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow20_col1\" class=\"data row20 col1\" >julia</td>\n",
       "                        <td id=\"T_b40d6ed8_fd74_11e9_82b9_701ce71031efrow20_col2\" class=\"data row20 col2\" >0.00%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x293f4750648>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data_pl['rank']=list(range(1,22))\n",
    "data_pl=data_pl[['rank','pl_','percentage']]\n",
    "#data_pl.style.format({\"percentage\":\"{:.2%}\"})\n",
    "data_pl.style.hide_index().format({\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_pl=data_pl.sort_values(by='percentage')\n",
    "\n",
    "# Pie chart, where the slices will be ordered and plotted counter-clockwise:\n",
    "labels = data_pl['pl_']\n",
    "sizes = data_pl['percentage']\n",
    "#explode = (0, 0.1, 0, 0)  # only \"explode\" the 2nd slice (i.e. 'Hogs')\n",
    "\n",
    "fig1, ax1 = plt.subplots(figsize=(15, 9))\n",
    "ax1.pie(sizes, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "plt.title(\"Programming Languages in China, Nov 2019\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word Cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud\n",
    "wc=WordCloud()\n",
    "wc_dict = {}\n",
    "for index, row in data_pl.iterrows():\n",
    "    wc_dict[row['pl_']]=row['percentage']\n",
    "\n",
    "\n",
    "wc=wc.generate_from_frequencies(wc_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "wc_dict['c/c++']=0.33\n",
    "wc=wc.generate_from_frequencies(wc_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "plt.imshow(wc, interpolation=\"bilinear\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
